Historical Surveillance Data Research Articles

A recent history of disease outbreaks in Kenya, 2007–2022: Findings from routine surveillance data

BackgroundAfrica reports the highest number of outbreaks globally, accounting for 39% of all outbreaks in 2022. The Integrated Disease Surveillance and Response strategy in Kenya ensures the reporting of outbreaks up to the national level. We present a summary of the burden of reported disease outbreaks in Kenya, 2007–2022.MethodsWe reviewed historical surveillance data, 2007–2022, summarized the annual caseload and deaths of reported outbreaks, and classified the outbreaks into 3 categories, that is high, moderate, and low burden. A nested Poisson regression model was fit to determine whether there was a significant increase in the number of diseases and counties reporting outbreaks over time.ResultsTwenty-three diseases were reported. COVID-19, cholera, epidemic malaria, kala-azar, and measles were associated with a high disease burden. The highest number of diseases reported in a single year was 10. We observed an increase in the number of outbreaks over time (IRR = 1.26, 95% CI [1.22–1.29], p < 0.001), and an increase in the number of counties reporting outbreaks over time (r = 0.97, p < 0.001).ConclusionThere was an increase in the frequency and geographic occurrence of outbreaks. The differences in outbreak occurrence between counties necessitate targeted and enhanced preventive, preparedness, and response interventions at the sub-national level to reduce the burden of outbreaks.

Insights and Challenges for the Development of Mosquito Control Action Thresholds Using Historical Mosquito Surveillance and Climate Datasets.

Strategies to advance action threshold development can benefit both civilian and military vector control operations. The Anastasia Mosquito Control District (AMCD) has curated an extensive record database of surveillance programs and operational control activities in St. Johns County, Florida, since 2004. A thorough exploratory data analysis was performed on historical mosquito surveillance and county-wide climate data to identify climate predictors that could be used in constructing proactive threshold models for initiating control of Aedes, Culex, and Anopheles vector mosquitoes. Species counts pulled from Centers for Disease Control and Prevention (CDC) light trap (2004-2019) and BG trap (2014-2019) collection records and climate parameters of temperature (minimum, maximum, average), rainfall, and relative humidity were used in two iterations of generalized linear models. Climate readings were incorporated into models 1) in the form of continuous measurements, or 2) for categorization into number of "hot," "wet," or "humid" days by exceedance of selected biological index threshold values. Models were validated with tests of residual error, comparison of model effects, and predictive capability on testing data from the two recent surveillance seasons 2020 and 2021. Two iterations of negative binomial regression models were constructed for 6 species groups: container Aedes (Ae. aegypti, Ae. albopictus), standing water Culex (Cx. nigripalpus, Cx. quinquefasciatus), floodwater Aedes (Ae. atlanticus, Ae. infirmatus), salt-marsh Aedes (Ae. taeniorhyncus, Ae. sollicitans), swamp water Anopheles (An. crucians), and a combined Total Mosquitoes group. Final significant climate predictors varied substantially between species groups. Validation of models with testing data displayed limited predictive abilities of both model iterations. The most significant climate predictors for floodwater Aedes, the dominant and operationally influential species group in the county, were either total precipitation or frequency of precipitation events (number of "wet" days) at two to four weeks before trap collection week. Challenges hindering the construction of threshold models were discussed. Insights gained from these models provide initial feedback for streamlining the AMCD mosquito control program and analytical recommendations for future modelling efforts of interested mosquito control programs, in addition to generalized guidance for deployed armed forces personnel with needs of mosquito control but lacking active surveillance programs.

Pharmacy on the front lines: A century of pandemic response in America

The history of American pharmacy contributions to pandemic responses can be described for five pandemics: 1918 (influenza A/H1N1 virus), 1957–1958 (H2N2 virus), 1968 (H3N2 virus), 2009 (H1N1pdm09 virus), and 2019–2023 (syndrome coronavirus-2 virus). Using historical surveillance data and published literature, this article provides opportunities to reflect on how the pharmacy profession played a role in preparedness and response.

Evaluation of the iCARE Nigeria Pilot Intervention Using Social Media and Peer Navigation to Promote HIV Testing and Linkage to Care Among High-Risk Young Men

Nigeria has the fourth-largest HIV epidemic globally, yet high levels of social stigma inhibit HIV testing among Nigerian youths and young men who have sex with men (MSM). To report pilot data from iCARE Nigeria (Intensive Combination Approach to Roll Back the Epidemic in Nigerian Adolescents), a combination intervention using social media and peer navigation to promote HIV testing and linkage to care among high-risk youths and young men (hereinafter referred to as young men), including predominantly young MSM. This nonrandomized controlled study assessed an organizational and community-level 12-month, preintervention-postintervention pilot trial of a combination intervention designed to increase HIV testing uptake, increase the rate of identified seropositive cases, and improve linkage to care among young men, including MSM, using social media outreach and peer navigation. Data were collected from June 1, 2019, to May 30, 2020. Participants were young men aged 15 to 24 years in the city of Ibadan, Nigeria, and surrounding areas. Frequencies and percentages were examined, and a Fisher exact test was used to evaluate outcomes compared with historical surveillance data. Linkage to care was defined as 2 clinic visits, including HIV confirmation, within 2 months of a positive rapid test result. Four peer navigators conducted social media outreach promoting sexual health and guiding individuals to HIV counseling and rapid testing in clinical, community, or home-based settings. Primary outcomes included the number of young men tested for HIV at university-based iCARE catchment clinics or by iCARE peer navigators in the community, the postintervention HIV seroprevalence of these groups, and linkage to care of participants diagnosed with HIV infection. A total of 339 participants underwent testing for HIV (mean [SD] age, 21.7 [1.9] years), with 283 (83.5%) referred through social media. The main referral sources for social media were WhatsApp (124 [43.8%]), Facebook (101 [35.7%]), and Grindr (57 [20.1%]). Regarding testing location, participants chose home (134 [39.5%]), community-based (202 [59.6%]), or clinic (3 [0.9%]) settings. Eighty-six participants reported no prior HIV testing. Thirty-six participants (10.6%) were confirmed as HIV seropositive; among those, 18 (50.0%) reported negative test results within the past year, and 31 (86.1%) were linked to care. In two 6-month follow-up periods, the intervention increased HIV testing by 42% and 31%, respectively, and seroprevalence increased compared with historical trends with odds ratios of 3.37 (95% CI, 1.43-8.02; P = .002) and 2.74 (95% CI, 1.10-7.11; P = .02), respectively. These findings suggest that use of iCARE Nigeria was associated with increased HIV testing and linkage to care in a high-risk, difficult-to-reach population, making it a promising combination intervention for young MSM. isrctn.org Identifier: ISRCTN94590823.

913. Re-engagement using Historical Hepatitis C Antibody Results: Is it Worth the Effort?

BackgroundThe World Health Organisation aim to eliminate hepatitis C (HCV) as a public health concern by 2030. One aspect of Public Health England’s (PHE) strategy to meet this target is to use historical surveillance data of anti-HCV positive patients identified by PHE to re-engage with offers of PCR testing and treatment if RNA-positive. Operational Delivery Networks (ODN) are responsible for enacting this initiative across 22 regions in England. We present an interim analysis and evaluation of the effectiveness of using this data to re-engage HCV-infected persons in the West Midlands ODN of England.MethodsA dataset of historical anti-HCV positive antibody patients provided to the West Midlands ODN by PHE was cross-referenced with HCV RNA data from 01/01/1996 to 01/01/2019 from 5 regional laboratories and regional treatment databases. If HCV RNA positive, letters were sent to the general practitioner and to the patient to invite them for further testing and, if necessary, treatment to achieve SVR. This received no additional funding or support and occurred in addition to the routine clinical workload.ResultsFrom a dataset of 4,540 anti-HCV antibody results, 31.7% (n=1,440) had a PCR result: 48.1% (n=693) were PCR positive for HCV RNA with no evidence of cure. 693 letters were sent to GPs from Oct 2019 to Feb 2020 with responses from 14.2% (n=99). From July to Oct 2020 only 212 patient letters were sent (due to significant interruption due to the COVID-19 pandemic) and 11.3% (n=24) replied by May 2021. 17 presented for PCR testing and 4 were found to be viraemic. To date, one patient has achieved SVR and three have completed treatment awaiting SVR.Re-Engagement Process Flow diagram of re-engagement of patients with historical antibody-positive results for hepatitis C virus. * Of the 17 deemed not suitable to contact by the GP: 4 treated elsewhere, 3 had negative PCR elsewhere, 1 was unknown reason, 2 were under care of another hospital, 7 had diedConclusionThe use of historical anti-HCV antibody results to re-engage people into testing and treatment for hepatitis C in this format is low yield. Rollout was limited by ongoing clinical work and the COVID-19 pandemic. Dedicated time and resources with a less restrictive cohort might improve yields.Disclosures All Authors: No reported disclosures

An experience using historical hepatitis C data to Re-Engage: Possibilities and pitfalls during the COVID-19 pandemic

ObjectivesPublic Health England (PHE) aims meet the WHO target to eliminate hepatitis C as a public health concern by 2030. One aspect of this strategy is to use historical surveillance data of anti-HCV positive patients identified by PHE to re-engage with offers of PCR testing and treatment if RNA-positive. Operational Delivery Networks (ODN), who deliver Hepatitis C treatment across 22 regions in England, are responsible for enacting this initiative. This study aims to evaluate the effectiveness of using this data with regional PCR results to re-engage HCV-infected persons in the West Midlands region of England. Study designA longitudinal prospective study using historical surveillance data. MethodsA dataset of historical anti-HCV positive antibody patients provided to the ODN by PHE was cross-referenced with HCV RNA data from 01/01/1996 to 01/01/2019 from five laboratories across the West Midlands. Letters were sent to the general practitioner and to the patients who were HCV RNA positive to invite them for repeat testing and treatment to achieve cure. ResultsFrom a dataset of 4540 anti-HCV antibody results, 31.7% (n=1440) had a PCR result: 48.1% (n=693) were PCR positive for HCV RNA. 693 letters were sent to GPs with responses from 14.2% (n=99). By May 2021, only 212 patient letters were sent (due to significant interruption by the COVID-19 pandemic) and 11.3% (n=24) replied, 17 presented for PCR testing and 4 were found to be viraemic. To date, one patient has achieved cure and three have completed treatment awaiting confirmation of cure. ConclusionThe use of historical anti-HCV antibody results can be used to successfully re-engage people into testing and treatment for hepatitis C, albeit with modest gains.

RELATIONSHIP OF PRECIPITATION AND HABITAT TO THE SPATIAL AND TEMPORAL ABUNDANCE OF AEDES ATLANTICUS AND AEDES INFIRMATUS IN ST. JOHNS COUNTY, FLORIDA

The purpose of this study was to perform descriptive and inferential analyses to better understand the presence of the abundant mosquito species Aedes atlanticus and Aedes infirmatus in St. Johns County, northeastern Florida. Historical surveillance data (2010-2019) obtained from Anastasia Mosquito Control District of St. Johns County, St. Augustine, FL, was organized to graph temporal mosquito abundance trends and inverse distance weighted (IDW) interpolation was used to map spatial distribution patterns of mosquitoes. Precipitation and habitat composition were investigated as spatiotemporal predictors of mosquito abundance using Pearson’s correlation statistics. There were considerable and inconsistent fluctuations in the population abundance of Ae. atlanticus and Ae. infirmatus across and within individual surveillance seasons during the last decade. Precipitation was significantly associated with total county-wide mosquito population counts by season (Ae. atlanticus, R = 0.810, p = 0.005; Ae. infirmatus, R = 0.850, p = 0.002), while the association with weekly mosquito population trends was inconsistently significant across species, lag time, and years. The proportion of surrounding land covered by upland forest, water, and agriculture was associated with species abundance at the spatial level of individual trap sites. Overall, the results identify that Ae. atlanticus and Ae. infirmatus share a spatiotemporal relationship and are similarly impacted by rainfall and habitat type. Findings of the study might help to inform improved surveillance by integrating IDW estimation maps with current district resources and improved knowledge of species’ ecology.

Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia

BackgroundDespite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world’s population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods.MethodsWe used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods.ResultsAll of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80–100% CDC; 57–100% weekly statistical) and low to moderate alarm specificities (25–40% CDC; 16–61% weekly statistical). Farrington variants had a wide range of scores (20–100% sensitivities; 16–100% specificities) and could achieve various balances between sensitivity and specificity.ConclusionsOf the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

The role of heterogenous environmental conditions in shaping the spatiotemporal distribution of competing Aedes mosquitoes in Panama: implications for the landscape of arboviral disease transmission

Monitoring the invasion process of the Asian tiger mosquito Aedes albopictus and its interaction with the contender Aedes aegypti, is critical to prevent and control the arthropod-borne viruses (i.e., Arboviruses) they transmit to humans. Generally, the superior ecological competitor Ae. albopictus displaces Ae. aegypti from most geographic areas, with the combining factors of biology and environment influencing the competitive outcome. Nonetheless, detailed studies asserting displacement come largely from sub-tropical areas, with relatively less effort being made in tropical environments, including no comprehensive research about Aedes biological interactions in Mesoamerica. Here, we examine contemporary and historical mosquito surveillance data to assess the role of shifting abiotic conditions in shaping the spatiotemporal distribution of competing Aedes species in the Republic of Panama. In accordance with prior studies, we show that Ae. albopictus has displaced Ae. aegypti under suboptimal wet tropical climate conditions and more vegetated environments within the southwestern Azuero Peninsula. Conversely, in the eastern Azuero Peninsula, Ae. aegypti persists with Ae. albopictus under optimal niche conditions in a dry and more seasonal tropical climate. While species displacement was stable over the course of two years, the presence of both species generally appears to fluctuate in tandem in areas of coexistence. Aedes albopictus was always more frequently found and abundant regardless of location and climatic season. The heterogenous environmental conditions of Panama shape the competitive outcome and micro-geographic distribution of Aedes mosquitoes, with potential consequences for the transmission dynamics of urban and sylvatic zoonotic diseases.

Genetic and biological characterization of Newcastle disease viruses circulating in Bangladesh during 2010-2017: further genetic diversification of class II genotype XIII in Southcentral Asia.

Newcastle disease virus (NDV) is endemic in Bangladesh and is a major threat to commercial poultry operations. While complete fusion (F) genes are recommended for molecular characterization and classification of NDV isolates, heretofore, only partial F gene data have been available for Bangladeshi NDVs. To this end, we obtained the full-length F gene coding sequences of 11 representative NDVs isolated in Bangladesh between 2010 and 2017. In addition, one of the viruses (MK934289/chicken/Bangladesh/C161/2010) was used in an experimental infection of chickens to establish the viral pathotype and study gross and microscopic lesions. Phylogenetic analysis provided evidence that all studied Bangladeshi isolates belong to genotype XIII.2 of class II NDVs. Six of the viruses were isolated between 2010 and 2017 and grouped together with isolates from neighbouring India during 2013-2016. Another four Bangladeshi isolates (2010-2016) formed a separate monophyletic branch within XIII.2 and showed high nucleotide distance from the isolates from India and the other six Bangladeshi viruses within the sub-genotype; however, none of these groups fulfils all classification criteria to be named as a separate sub-genotype. The eleventh Bangladeshi virus studied here (C162) was genetically more distant from the remaining isolates. It out-grouped the viruses from sub-genotypes XIII.2.1 and XIII.2.2 and showed more than 9.5 % nucleotide distance from all genotype XIII sub-genotypes. This isolate may represent an NDV variant that is evolving independently from the other viruses in the region. The experimental infection in chickens revealed that the tested isolate (C161) is a velogenic viscerotropic virus. Massive haemorrhages, congestion and necrosis in different visceral organs, and lymphoid depletion in lymphoid tissues, typical for infection with velogenic NDV, were observed. Our findings demonstrate the endemic circulation of sub-genotype XIII.2 in Southcentral Asia and further genetic diversification of these viruses in Bangladesh and neighbouring India. This constant evolution of the viruses may lead to the establishment of new genetic groups in the region. Additional historical and prospective virus and surveillance data from the region and neighbouring countries will allow a more detailed epidemiological inference.

Hybrid Machine Learning and Estimation-Based Flight Trajectory Prediction in Terminal Airspace

For air traffic management, trajectory prediction plays an important role as the predicted trajectory information is used in crucial tasks for the safety and efficiency of air traffic operations, such as conflict detection and resolution, scheduling, and sequencing. In this paper, we propose a framework for trajectory prediction in terminal airspace by combining a machine learning-based method and a physics-based estimation method. A trajectory prediction model based on machine learning is trained from historical surveillance data to represent the collective behavior of a set of flight trajectories, from which the data-driven prediction can be obtained as the expected future behavior of an incoming flight. A physics-based estimation algorithm called Residual-Mean Interacting Multiple Models (RM-IMM) then incorporates the machine learning prediction as a pseudo-measurement to account for the current motion of the aircraft. The proposed framework is tested, with real air traffic surveillance data, by predicting the future state information of the flights for real-time air traffic control applications. The results show that the proposed framework produces a greatly improved prediction accuracy compared to the two existing machine learning-based algorithms.

Prioritisation of areas for early detection of southward movement of arctic fox rabies based on historical surveillance data in Quebec, Canada.

Arctic rabies virus variant (ARVV) is enzootic in Quebec (Canada) north of the 55th parallel. With climate change, increased risk of re-incursion of ARVV in more densely populated southern regions raises public and animal health concerns. The objective of this study was to prioritise geographical areas to target for an early detection of ARVV incursion south of the 55th parallel based on the historical spatio-temporal trends of reported rabies in foxes in Quebec. Descriptive analyses of fox rabies cases from 1953 to 2017 were conducted. Three periods show increases in the number of fox rabies cases in southern regions and indicate incursion from northern areas or neighbouring provinces. The available data, particularly in central and northern regions of the province, were scarce and of low spatial resolution, making it impossible to identify the path of spread with precision. Hence, we investigated the use of multiple criteria, such as historical rabies cases, human population density and red fox (Vulpes vulpes) relative abundance, to prioritise areas for enhanced surveillance. This study underscores the need to define and maintain new criteria for selecting samples to be analysed in order to detect rapidly ARVV cases outside the current enzootic area and any potential re-incursion of the virus into central and southern regions of the province.

Hepatitis A Person-to-Person Outbreaks: Epidemiology, Morbidity Burden, and Factors Associated With Hospitalization—Multiple States, 2016–2019

Since 2016, the United States has experienced person-to-person hepatitis A outbreaks unprecedented in the vaccine era. The proportion of cases hospitalized in these outbreaks exceeds historical national surveillance data. We described the epidemiology, characterized the reported increased morbidity, and identified factors associated with hospitalization during the outbreaks by reviewing a 10% random sample of outbreak-associated hepatitis A cases in Kentucky, Michigan, and West Virginia-3 heavily affected states. We calculated descriptive statistics and conducted age-adjusted log-binomial regression analyses to identify factors associated with hospitalization. Participants in the random sample (n = 817) were primarily male (62.5%) with mean age of 39.0 years; 51.8% were hospitalized. Among those with available information, 73.2% reported drug use, 14.0% were experiencing homelessness, 29.7% were currently or recently incarcerated, and 61.6% were epidemiologically linked to a known outbreak-associated case. Residence in Michigan (adjusted risk ratio [aRR] = 1.8), being a man who has sex with men (aRR = 1.5), noninjection drug use (aRR = 1.3), and homelessness (aRR = 1.3) were significantly (P < .05) associated with hepatitis A-related hospitalization. Our findings support current Advisory Committee on Immunization Practices recommendations to vaccinate all persons who use drugs, men who have sex with men, and persons experiencing homelessness against hepatitis A.

Estimating and projecting the number of new HIV diagnoses and incidence in Spectrum's case surveillance and vital registration tool

The Joint United Nations Programme on HIV/AIDS-supported Spectrum software package is used by most countries worldwide to monitor the HIV epidemic. In Spectrum, HIV incidence trends among adults (aged 15-49 years) are derived by either fitting to seroprevalence surveillance and survey data or generating curves consistent with case surveillance and vital registration data, such as historical trends in the number of newly diagnosed infections or AIDS-related deaths. This article describes development and application of the case surveillance and vital registration (CSAVR) tool for the 2019 estimate round. Incidence in CSAVR is either estimated directly using single logistic, double logistic, or spline functions, or indirectly via the 'r-logistic' model, which represents the (log-transformed) per-capita transmission rate using a logistic function. The propensity to get diagnosed is assumed to be monotonic, following a Gamma cumulative distribution function and proportional to mortality as a function of time since infection. Model parameters are estimated from a combination of historical surveillance data on newly reported HIV cases, mean CD4 at HIV diagnosis and estimates of AIDS-related deaths from vital registration systems. Bayesian calibration is used to identify the best fitting incidence trend and uncertainty bounds. We used CSAVR to estimate HIV incidence, number of new diagnoses, mean CD4 at diagnosis and the proportion undiagnosed in 31 European, Latin American, Middle Eastern, and Asian-Pacific countries. The spline model appeared to provide the best fit in most countries (45%), followed by the r-logistic (25%), double logistic (25%), and single logistic models. The proportion of HIV-positive people who knew their status increased from about 0.31 [interquartile range (IQR): 0.10-0.45] in 1990 to about 0.77 (IQR: 0.50-0.89) in 2017. The mean CD4 at diagnosis appeared to be stable, at around 410 cells/μl (IQR: 224-567) in 1990 and 373 cells/μl (IQR: 174-475) by 2017. Robust case surveillance and vital registration data are routinely available in many middle-income and high-income countries while HIV seroprevalence surveillance and survey data may be scarce. In these countries, CSAVR offers a simpler, improved approach to estimating and projecting trends in both HIV incidence and knowledge of HIV status.

Development of a mechanistic dengue simulation model for Guangzhou.

Dengue infection in China has increased dramatically in recent years. Guangdong province (main city Guangzhou) accounted for more than 94% of all dengue cases in the 2014 outbreak. Currently, there is no existing effective vaccine and most efforts of control are focused on the vector itself. This study aimed to evaluate different dengue management strategies in a region where this disease is emerging. This work was done by establishing a dengue simulation model for Guangzhou to enable the testing of control strategies aimed at vector control and vaccination. For that purpose, the computer-based dengue simulation model (DENSiM) together with the Container-Inhabiting Mosquito Simulation Model (CIMSiM) has been used to create a working dengue simulation model for the city of Guangzhou. In order to achieve the best model fit against historical surveillance data, virus introduction scenarios were run and then matched against the actual dengue surveillance data. The simulation model was able to predict retrospective outbreaks with a sensitivity of 0.18 and a specificity of 0.98. This new parameterisation can now be used to evaluate the potential impact of different control strategies on dengue transmission in Guangzhou. The knowledge generated from this research would provide useful information for authorities regarding the historic patterns of dengue outbreaks, as well as the effectiveness of different disease management strategies.

Real-time estimation of the influenza-associated excess mortality in Hong Kong.

Statistical models are commonly employed in the estimation of influenza-associated excess mortality that, due to various reasons, is often underestimated by laboratory-confirmed influenza deaths reported by healthcare facilities. However, methodology for timely and reliable estimation of that impact remains limited because of the delay in mortality data reporting. We explored real-time estimation of influenza-associated excess mortality by types/subtypes in each year between 2012 and 2018 in Hong Kong using linear regression models fitted to historical mortality and influenza surveillance data. We could predict that during the winter of 2017/2018, there were ~634 (95% confidence interval (CI): (190, 1033)) influenza-associated excess all-cause deaths in Hong Kong in population ⩾18 years, compared to 259 reported laboratory-confirmed deaths. We estimated that influenza was associated with substantial excess deaths in older adults, suggesting the implementation of control measures, such as administration of antivirals and vaccination, in that age group. The approach that we developed appears to provide robust real-time estimates of the impact of influenza circulation and complement surveillance data on laboratory-confirmed deaths. These results improve our understanding of the impact of influenza epidemics and provide a practical approach for a timely estimation of the mortality burden of influenza circulation during an ongoing epidemic.

Surveillance of animal diseases through implementation of a Bayesian spatio-temporal model: A simulation example with neurological syndromes in horses and West Nile Virus

A potentially sensitive way to detect disease outbreaks is syndromic surveillance, i.e. monitoring the number of syndromes reported in the population of interest, comparing it to the baseline rate, and drawing conclusions about outbreaks using statistical methods. A decision maker may use the results to take disease control actions or to initiate enhanced epidemiological investigations. In addition to the total count of syndromes there are often additional pieces of information to consider when assessing the probability of an outbreak. This includes clustering of syndromes in space and time as well as historical data on the occurrence of syndromes, seasonality of the disease, etc. In this paper, we show how Bayesian theory for syndromic surveillance applies to the occurrence of neurological syndromes in horses in France. Neurological syndromes in horses may be connected e.g. to West Nile Virus (WNV), a zoonotic disease of growing concern for public health in Europe. A Bayesian method for spatio-temporal cluster detection of syndromes and for determining the probability of an outbreak is presented. It is shown how surveillance can be performed simultaneously for a specific class of diseases (WNV or diseases similar to WNV in terms of the information available to the system) and a non-specific class of diseases (not similar to WNV in terms of the information available to the system). We also discuss some new extensions to the spatio-temporal models and the computational algorithms involved. It is shown step-by-step how data from historical WNV outbreaks and surveillance data for neurological syndromes can be used for model construction. The model is implemented using a Gibbs sampling procedure, and its sensitivity and specificity is evaluated. Finally, it is illustrated how predictive modelling of syndromes can be useful for decision making in animal health surveillance.

Geospatial Analysis and Seasonal Distribution of West Nile Virus Vectors (Diptera: Culicidae) in Southern Ontario, Canada.

The purpose of this study was to establish geospatial and seasonal distributions of West Nile virus vectors in southern Ontario, Canada using historical surveillance data from 2002 to 2014. We set out to produce mosquito abundance prediction surfaces for each of Ontario’s thirteen West Nile virus vectors. We also set out to determine whether elevation and proximity to conservation areas and provincial parks, wetlands, and population centres could be used to improve our model. Our results indicated that the data sets for Anopheles quadrimaculatus, Anopheles punctipennis, Anopheles walkeri, Culex salinarius, Culex tarsalis, Ochlerotatus stimulans, and Ochlerotatus triseriatus were not suitable for geospatial modelling because they are randomly distributed throughout Ontario. Spatial prediction surfaces were created for Aedes japonicus and proximity to wetlands, Aedes vexans and proximity to population centres, Culex pipiens/restuans and proximity to population centres, Ochlerotatus canadensis and elevation, and Ochlerotatus trivittatus and proximity to population centres using kriging. Seasonal distributions are presented for all thirteen species. We have identified both when and where vector species are most abundant in southern Ontario. These data have the potential to contribute to a more efficient and focused larvicide program and West Nile virus awareness campaigns.

The measles epidemic trend over the past 30 years in a central district in Shanghai, China.

BackgroundMeasles vaccination over the past 50 years has greatly reduced the incidence of measles. However, measles among migrants and the resulting changes in epidemiological characteristics have brought new challenges to the elimination of measles. We aim to describe the measles epidemic trend over the past 30 years in a central district in Shanghai, China.MethodsThe present study was conducted in the Jing’an District, which is located in the center of Shanghai. Based on historical surveillance data of measles, we calculated the incidence of measles among local residents and migrants separately. Next, we classified all of the cases of the measles among local residents between 1984 and 2015 into 8 age groups and 5 birth cohorts. Finally, we calculated the measles incidence in each time period by the different age groups and birth cohorts, to understand the measles epidemic trend over past 30 years in the Jing'an District.ResultsA total of 103 cases of measles were reported from the Jing’an District, Shanghai, from 1984 to 2015. For infants less than 1 year of age and adults over 30 years of age, the incidence of measles continued to rise over the past 30 years. For a specific birth cohort, the incidence of measles after measles vaccination declined initially, and was then followed by a rebound.ConclusionsThe incidence of measles in older adults and infants increased in some developed regions, which slows the process of measles elimination. This suggested that the population immunity against measles after measles vaccination would gradually reduce with time. We recommend supplemental immunization against measles in adults in order to reduce the immunity decline, especially for migrants.

Influenza vaccine efficacy trials: a simulation approach to understand failures from the past.

The success of a seasonal influenza vaccine efficacy trial depends not only upon the design but also upon the annual epidemic characteristics. In this context, simulation methods are an essential tool in evaluating the performances of study designs under various circumstances. However, traditional methods for simulating time-to-event data are not suitable for the simulation of influenza vaccine efficacy trials because of the seasonality and heterogeneity of influenza epidemics. Instead, we propose a mathematical model parameterized with historical surveillance data, heterogeneous frailty among the subjects, survey-based heterogeneous number of daily contact, and a mixed vaccine protection mechanism. We illustrate our methodology by generating multiple-trial data similar to a large phase III trial that failed to show additional relative vaccine efficacy of an experimental adjuvanted vaccine compared with the reference vaccine. We show that small departures from the designing assumptions, such as a smaller range of strain protection for the experimental vaccine or the chosen endpoint, could lead to smaller probabilities of success in showing significant relative vaccine efficacy.